Hardware-efficient deep convolutional neural networks

ABSTRACT

Systems, methods, and computer media for implementing convolutional neural networks efficiently in hardware are disclosed herein. A memory is configured to store a sparse, frequency domain representation of a convolutional weighting kernel. A time-domain-to-frequency-domain converter is configured to generate a frequency domain representation of an input image. A feature extractor is configured to access the memory and, by a processor, extract features based on the sparse, frequency domain representation of the convolutional weighting kernel and the frequency domain representation of the input image. The feature extractor includes convolutional layers and fully connected layers. A classifier is configured to determine, based on extracted features, whether the input image contains an object of interest. Various types of memory can be used to store different information, allowing information-dense data to be stored in faster (e.g., faster access time) memory and sparse data to be stored in slower memory.

BACKGROUND

A neural network implements a computational approach based to someextent on the central nervous systems of animals Neural networks can beused in artificial-intelligence-based approaches to machine learningthat may be applied, for example, in speech recognition, imagerecognition/object detection, and other areas. Neural networks arecomposed of interconnected “neurons” that make decisions based on inputvalue(s) and threshold(s). Convolutional neural networks are a class ofneural networks that typically involve three stages ofcomputation—convolutional layer(s), fully connected layer(s), andclassifier(s).

Although convolutional neural networks perform well compared with morelimited modeling-based approaches to machine learning, implementingconvolutional neural networks in hardware incurs a high energy andcomputational complexity cost. For example, convolutional layerstypically involve a high computational complexity, and fully connectedlayers typically involve a high memory storage cost. These factors,among others, deter implementation of convolutional neural networks inpower-constrained devices such as wearables and mobile devices.

SUMMARY

Examples described herein relate to hardware-efficient implementationsof deep convolutional neural networks. A memory can be configured tostore a sparse, frequency domain representation of a convolutionalweighting kernel. A time-domain-to-frequency-domain converter can beconfigured to, by a processor, generate a frequency domainrepresentation of an input image. The input image can be a video frameor image captured by a camera. A feature extractor can be configured toaccess the memory and, by the processor, extract features based on thesparse, frequency domain representation of the convolutional weightingkernel and the frequency domain representation of the input image. Aclassifier can be configured to, by the processor, determine, based onextracted features, whether the input image contains an object ofinterest.

In some examples, multiple memories of different memory types are usedto store different information, allowing information-dense data to bestored in faster (e.g., faster access time) and higher energyconsumption memory and sparse data to be stored in slower (but lowerenergy consumption) memory. For example, a slower memory type (or alower energy consumption memory type) can be used to store sparsematrices of the frequency domain representation of the convolutionalweighting kernel, and one or more faster memory types can be used tostore a dense matrix of the frequency domain representation of theconvolutional weighting kernel, fully connected layer coefficients,and/or image/video frame coefficients.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

The foregoing and other objects, features, and advantages of the claimedsubject matter will become more apparent from the following detaileddescription, which proceeds with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example hardware-efficient convolutionalneural network system.

FIG. 2 is a block diagram of an example hardware-efficient convolutionalneural network system that includes two types of memory.

FIG. 3 is a diagram illustrating an example operational flow of ahardware-efficient deep convolutional neural network.

FIG. 4 is a block diagram illustrating example hardware and an exampleoperational flow of an example hardware-efficient convolutional neuralnetwork system.

FIG. 5 is a flowchart of an example image recognition method in aconvolutional neural network.

FIG. 6 is a flowchart of an example image recognition method in aconvolutional neural network in which the nonlinear function applied inthe convolutional layers is a frequency domain nonlinear function.

FIG. 7 is a flowchart of an example image recognition method in aconvolutional neural network in which the dense matrices of the kernelsof the convolutional layers are multiplied by the output of the lastconvolutional layer.

FIG. 8 is a diagram of an example computing system in which somedescribed embodiments can be implemented.

FIG. 9 is an example mobile device that can be used in conjunction withthe technologies described herein.

FIG. 10 is an example cloud-supported environment that can be used inconjunction with the technologies described herein.

DETAILED DESCRIPTION

Using the systems, methods, and computer-readable media describedherein, deep convolutional neural networks can be efficientlyimplemented in hardware. Unlike conventional implementations ofconvolutional neural networks that typically have high energy andcomputational costs, the described examples allow convolutional neuralnetworks to be used in power-constrained devices such as wearables andmobile devices. As specific examples, hardware efficient convolutionalneural networks can be implemented in an augmented or virtual realityheadset or mobile device application in which battery life is limited.

In the convolutional layers of a convolutional neural network, theconvolution operation (e.g., convolving an image with a weightingkernel) is conventionally computationally intensive because convolutionis a mathematically complex operation. In the described examples, theconvolutional weighting is done in the Fourier (frequency) domain, whichsubstantially reduces the complexity of the convolutional weightingstep. Memory and computational requirements are also reduced in thedescribed examples by representing the convolutional weighting kernel asa sparse, frequency domain representation (e.g., one or more sparsematrices and a dense matrix). The sparse matrices, which are informationsparse and have a smaller storage size than the dense matrix, can bestored in memory and accessed in each convolutional layer. The densematrix, which is information dense and has a larger storage size, can beapplied after the convolutional layers, greatly reducing thecomputational cost and complexity of the convolutional layers. In someof the described examples, additional operations are performed in thefrequency domain, allowing the application of the dense matrix to befurther delayed and thus reducing the computational and memory cost.Additional examples are described in detail below with reference toFIGS. 1-10.

Overview of Neural Networks

As discussed briefly above, neural networks are composed ofinterconnected “neurons” that make decisions based on input value(s) andthreshold(s). At a neuron, a non-linear function (also referred to as anactivation function) is applied to an input, and the output of thenon-linear function is compared to a threshold. Example non-linearfunctions include a rectified linear unit (ReLU), hyperbolic tangent(tan h), sigmoid function, or other non-linear function. The neuron can,for example, provide an output of “1” if the value of the non-linearfunction applied to the input is greater than the threshold or an outputof “0” if the value of the non-linear function applied to the input isless than the threshold.

The neurons in a neural network can have different levels ofconnectivity. In a fully connected neural network, each input isprovided to each neuron (or the neurons are otherwise eachinterconnected with every other neuron). In a partially connected neuralnetwork, an input is provided to one or more neurons, but each input istypically not provided to each neuron (or the neurons are interconnectedwith some other neurons but not all other neurons). Other types ofconnectivity include arbitrary connectivity and neighbor connectivity aswell as convolutional connectivity, discussed below. The greater theconnectivity between neurons, the greater the “richness” of thethresholds, allowing the neurons to capture more information. Forneurons receiving multiple inputs, the non-linear function is typicallyapplied to all of the inputs.

As an example, a neural network can be represented as a function f(Σw_(i), x_(i), t_(j)), where each input x_(i) has an associated weightw_(i), and each neuron has a threshold t_(j). At individual neurons,w_(i)x_(i) is computed, the non-linear function is applied, and theresult is compared to the threshold t_(j). Using tan h as the nonlinearfunction results in the following example comparison:

tan h(B _(i) +B ₀ Σw _(i) x _(i))>t _(j)  (1)

where B₀ and B_(i) are constants that are used to maintain the limits ofthe hyperbolic tangent function.

Neural networks can be used in machine learning and are an example of anartificial-intelligence-based approach to machine learning (as opposedto a modeling-based approach in which a model is specified and variousparameters and features of the model are learned). As an example, aneural network can be used to perform image or object recognition. Aninput image can be converted to an input vector of image pixel values.In a fully connected neural network, each of the pixel values in theinput vector is provided to each neuron. A non-linear function isapplied to the pixel values at each neuron, and each neuron outputs avalue by comparing the result of the non-linear function to the one ormore thresholds. The output values from the neurons form an outputvector.

The process of creating the output vector from the input vector is knownas feature extraction. Unlike model-based approaches that requiredifferent approaches to feature extraction for different types of inputdata, neural-network based feature extraction can be applied to avariety of data with known or unknown characteristics, including speechamplitude data, seismic data, or other sensor data.

The output vector can be provided to a classifier (e.g., a model-basedmachine learning classifier). The classifier can implement, for example,a support vector machine, decision tree, Fisher's linear discriminant,linear discriminant analysis (LDA), or other classification approach.The classifier analyzes the output vector and classifies the input imageas one of a group of classes. In a binary classifier, for example, animage could be classified as either containing (an output of “1”) anobject of interest (e.g., a face) or not containing (an output of “0”)the object of interest.

A neural network is typically trained to determine neuron thresholds andclassifier model parameters. Input data and available classifier outputlabels are provided to a training algorithm which attempts to minimizeoutput error over all classifier output labels. Parameter values andthresholds are found that result in the minimum achievable error.

Overview of Convolutional Neural Networks

A convolutional neural network is a type of neural network in which theneurons have partial connectivity in a particular manner (“convolutionalconnectivity”). In a convolutional neural network, a two-dimensional(2D) vector can be used as input. The 2D input vector is multiplied(e.g., element-wise multiplication) by a three-dimensional (3D) kernelof weights. A 2D window of pixels having the same 2D dimensions as the3D kernel of weights can be incremented across the input vector. Foreach increment, the pixel values of the input window are multiplied bythe 3D kernel of weights and an output value corresponding to the 2Dwindow is generated. A 3D input can also be provided to a convolutionalneural network. For example, an input image can be represented as three2D vectors (one for each of red, green, and blue) that are provided to aconvolutional neural network.

Deep neural networks have multiple layers, which adds richness to theparameters and thresholds of the neurons, classifiers, and othercomponents of the deep neural networks. Each layer can have a differenttype of connectivity. Individual layers can include convolutionalweighting, non-linear transformation, response normalization, and/orspatial pooling.

As an example, consider a 3D volume representation of an input layerthat, in a convolutional weighting layer, is transformed into another 3Dvolume feeding subsequent convolutional weighting layers and eventuallyone or more fully connected layers. Various combinations ofconvolutional layers, fully connected layers, or layers having otherconnectivity can be used. Various layers can also use max pooling, inwhich the maximum of a small group is selected as the output (e.g., themaximum value of four adjacent output values is used as the outputvalue). During a 3D convolution weighting stage, a 3D input volume ofpixels of dimensionality N×N×D are convolved with H kernels of dimensionk×k×D and a stride S (linear step offset). Each 3D kernel is shifted ina sliding-window-like fashion with a stride across the input volume.During each shift, every weight belonging to the 3D kernel can bemultiplied and added with every pair-wise input element from theoverlapping region of the 3D input volume.

The entire 3D convolution process can be broken down as a sequence ofmultiple 2D convolutions. A 2D convolution is a mathematical operationfrequently used in modern image processing. In 2D convolution, a windowof some finite size and shape (also known as support) is scanned acrossthe image. The output pixel value is computed as the weighted sum of theinput pixels within the window where the weights are the values of thefilter assigned to every pixel of the window itself. The window with itsweights is called the convolution weighting kernel (or simply thekernel). This leads to the following finite sum:

$\begin{matrix}{{c\left\lbrack {m,n} \right\rbrack} = {{{a\left\lbrack {m,n} \right\rbrack} \otimes {h\left\lbrack {m,n} \right\rbrack}} = {\sum\limits_{j = 0}^{J - 1}{\sum\limits_{k = 0}^{K - 1}{{h\left\lbrack {j,k} \right\rbrack}{a\left\lbrack {{m - j},{n - k}} \right\rbrack}}}}}} & (2)\end{matrix}$

where, c [m,n] is the output pixel at location m, n, the input pixel atlocation j, k is a [j, k] and the weighting kernel at that position is h[j, k]. Boundary conditions during 2D convolution can be handled usingzeros, folded pixels, or repeating the pixels at the boundary of theimage.

As a specific example, assume a 224×224 pixel image with three layers(representing red, green, and blue values) and a moving window of 11×11pixels, which represents two dimensions of an 11×11×32 kernel. Thewindow can move one pixel at a time or move with a stride of greaterthan one pixel. With a stride of four, the output is 55 (or 56)pixels×55 pixels with a depth of 96 pixels (the 32 pixels of the kerneldepth×3 (one for each of the red, green, and blue layers)). Additionallayers can then also be implemented. As opposed to a convolutionallayer, a “dense” layer is a fully connected layer. The size of thestride, kernel size, etc. are design parameters that can be selectedthrough trial and error, empirical observations, etc.

Typical machine-learning applications operate in two stages. First isthe training stage, which is both data and computation intensive, andtraditionally involves a distributed, high performance data centerarchitecture. The second stage, which is called the testing stage, onthe other hand, typically uses a small amount of input (e.g., sensordata) and produces a small output (e.g., labels). However, the testingstage typically involves intense computation on a single set ofclosely-knit machines. Convolutional neural networks used in a machinelearning context also involve training and testing.

For the testing stage of a typical convolutional neural network, threemain types of computation are performed—convolutional layers, fullyconnected layers, and classifiers. The classifiers tend to becomputationally benevolent and inexpensive. The convolutional layerstend to have the highest computational complexity due to the numerousconvolutions involved. The fully connected layers, on the other hand,typically involve only multiplications but raise the need for a largeamount of storage to handle the kernel weights. Thus, althoughconvolutional neural network based testing approaches can providereal-time operation and high algorithmic accuracy, conventionalconvolutional neural networks are computationally complex and require alarge amount of memory. Both of these factors lead to high costs inpower and energy.

Example Implementations

In the described examples, the convolutional weighting performed in theconvolutional layers is done in the Fourier (frequency) domain.Convolution in the time domain can be converted to multiplication in thefrequency domain, which reduces the complexity of convolutionalweighting and results in improved device processing speed and reducedpower consumption. The described examples can also reduce the memoryrequirements of convolutional neural networks.

FIG. 1 illustrates a convolutional neural network system 100 implementedon one or more computing device(s) 102. Computing device(s) 102 includesprocessor(s) 104. A memory 106 is configured to store a sparse,frequency domain representation 108 of a convolutional weighting kernel.In an example sparse representation, an initial data matrix isrepresented as one or more sparse matrices, in which much of the dataare zeros (also referred to as “information sparse”) and a dense matrix,in which much of the data are non-zero values (also referred to as“information dense”). The sparse matrix or matrices multiplied by thedense matrix is equal to the initial data matrix. Determining a sparserepresentation is also referred to as sparse matrix decomposition.Sparse matrix decomposition can be done using a range of techniques,including constrained dictionary learning, non-negative matrixfactorization, low-rank expression, vector quantization, and others.Sparse representation reduces overall storage space and can also be usedto represent, for example, coefficients in fully connected layers.

A time-domain-to-frequency-domain converter 110 is configured to, byprocessor(s) 104, generate a frequency domain representation of an inputimage 112. Time-domain-to-frequency-domain converter 110 can, forexample, determine the Fast Fourier Transform (FFT) or other transformof input image 112.

A feature extractor 114 is configured to, by processor(s) 104, accessmemory 106 and extract a plurality of features 116 from input image 112.Feature extractor 114 is configured to extract features 116 based atleast in part on sparse, frequency domain representation 108 of theconvolutional weighting kernel and the frequency domain representationof input image 112. Although convolutional frequency domain operations(multiplication) are less computationally intense than convolutionaltime domain operations (convolution), frequency domain operations addthe computation of the Fourier and inverse Fourier transforms, whichincreases computational cost.

The FFT is an efficient way of transforming an image to the frequencydomain. The FFT has a complexity of Order(MNlog(MN)) for an N×N imageconvolved with an MxM kernel. FFT-based multiplication thus speeds uptime-domain convolution for large enough kernel sizes becausetime-domain convolution has an execution time proportional to N²M²,which is much higher than Order(MNlog(MN)). Plotting these complexitiesshows that FFT-based convolution can be inefficient for kernel sizesthat are very small. There are also various ways of speeding up the FFTso that convolution computation speed can be increased even for smallkernel sizes.

Feature extractor 114 can be configured, however, to perform additionaloperations in the frequency domain to limit the number of Fourier andinverse Fourier transforms that must be performed. In such examples,rather than performing, for example, an FFT and an inverse FFT in eachconvolutional layer, the operations of each layer can be performed inthe frequency domain to limit the FFT to the initial FFT of input image112 and an inverse FFT after the convolutional (or fully connected)layers. Feature extractor 114 can comprise a plurality of convolutionallayers and a plurality of fully connected layers. Example convolutionaland fully connected layers are illustrated in detail in FIGS. 3 and 4.

In some examples, feature extractor 114 can be configured to, in a firstconvolutional layer, multiply the frequency domain representation ofinput image 112 by the one or more sparse matrices and apply a nonlinearfunction to a result of the multiplication. Feature extractor 114 canalso be configured to perform spatial pooling, max normalization, and/orother functions. In some examples, the nonlinear function is a frequencydomain nonlinear function. Determination of a frequency domain nonlinearfunction is discussed below.

A second convolutional layer of feature extractor 114 can be configuredto multiply a frequency domain output of the first convolutional layerby the one or more sparse matrices and apply a nonlinear function to aresult of the multiplication. In such examples, an output from oneconvolutional layer is an input to a subsequent convolutional layer. Anoutput of a final convolutional layer can also be input to a first fullyconnected layer, and an output of the first fully connected layer canthen be an input a subsequent fully connected layer, etc.

As discussed above, feature extractor 114 saves computing resources bybeing configured to perform multiplication in the frequency domainrather than convolution in the time domain. Feature extractor 114 cansave additional computing and memory resources by delayingmultiplication by the dense matrix of sparse, frequency domainrepresentation 108 until after processing in the plurality ofconvolutional layers and/or processing in the fully connected layers.Each convolutional layer typically has a corresponding dense matrix andone or more sparse matrices (that together represent the convolutionalweighting kernel for the layer), and in some examples, the densematrices for all of the convolutional layers are multiplied after thelast convolutional layer or last fully connected layer.

A classifier 118 is configured to, by processor(s) 104, determine, basedon extracted features 116, whether input image 112 contains an object ofinterest, as represented by object recognition result 120. Classifier118 can be, for example, a binary classifier that determines a “1” or“0” indicating that an object of interest is either present or notpresent or a multiclass classifer. System 100 can include additionalmemory (not shown) of a same or different types, and/or memory 106 canbe comprised of multiple individual memory units of a same or differenttypes. An example of such a configuration is discussed with respect toFIG. 2. Although system 100 illustrates an input image 112, alternativeinput data, such as audio data or other sensor data can be provided asan input in addition or in place of input image 112. In such examples,classifier 118 is configured to determine whether the audio or otherinput contains an aspect of interest (e.g. word or sound of interest).

FIG. 2 illustrates a convolutional neural network system 200 implementedon one or more computing device(s) 202. System 200 includes severalcomponents that are similar to those illustrated in system 100 of FIG.1, including processor(s) 204, time-domain-to-frequency-domain converter206, feature extractor 208, and classifier 210. A camera(s) 212 isconfigured to capture input images or video frames that are provided totime-domain-to-frequency-domain converter 206. Camera(s) 212 can be anRGB, infrared, or other camera. System 200 can include various othersensors (not shown). Camera(s) 212, other sensors, and computingdevice(s) 202 can be part of a virtual reality or augmented realitysystem.

A first memory 214 is configured to store one or more sparse matrices216 of a sparse, frequency domain representation of a convolutionalweighting kernel. A second memory 218 is configured to storecoefficients 220 for fully connected layers and/or the dense matrix 222of the sparse, frequency domain representation. Second memory 218 is ofa second memory type and first memory 214 is of a first memory type thathas a slower access time and/or lower energy consumption than the secondmemory type. For example, second memory 218 can be SRAM (static randomaccess memory), and first memory 214 can be DRAM (dynamic random accessmemory). Less-expensive DRAM can be used for first memory 214 becausethe speed (access time) constraints of DRAM are less important for thesmall amount of data in the sparse matrices 216. In contrast, fullyconnected coefficients 220 and dense matrix 222 are information denseand benefit more from the more expensive but faster SRAM.

As another example, first memory 214 can be a memory type that has alower energy consumption (and lower speed) than SRAM, such asspin-transfer torque (STT) RAM, embedded DRAM (eDRAM), and non-volatilememories such as phase change memory (PCM) or embedded PCM (ePCM). As isthe case with DRAM as discussed above, the slower access time withmemory types such as STT RAM, etc. is less important because of thesmall amount of data in the sparse matrices 216. Additionally, memoriessuch as STT RAM also use less energy than DRAM, further extending thelife of limited power supplies for mobile devices, wearables, and otherpower-constrained devices.

In some examples, system 200 includes a third memory configured to storeinput image coefficients or other data of an intermediate informationdensity. The third memory is of a third memory type and has an accesstime (or energy consumption level) between the access time or energyconsumption level of the first memory type and the access time or energyconsumption level of the second memory type. The third memory can be,for example, a structured memory such as content-addressable memory(CAM). In some examples, a single type of memory can be used for firstmemory 214, second memory 218, and any additional memory (e.g., a thirdmemory as discussed above).

FIG. 3 illustrates a deep convolutional neural network 300. The red,green, and blue portions of an input image 302 (shown as three parallelrectangles) are provided as an input to deep convolutional neuralnetwork 300. Polyphase filtering 304 is performed, and the result isprovided to a first convolutional layer 306. In some examples, polyphasefiltering 304 is omitted. An FFT operation 308 is performed on the inputto first convolutional layer 306, and the resulting frequency domainrepresentation is multiplied in convolutional weighting portion 310 bysparse matrices 312 of a frequency domain representation of aconvolutional weighting kernel 314.

Convolutional weighting kernel 314 is pre-determined and transformed tothe frequency domain (e.g., by using an FFT). In order to multiplyconvolutional weighting kernel by the transformed input image in firstconvolutional layer 306, the frequency domain representation of theconvolutional weighting kernel is expanded using additional zero valuesuntil the kernel and the transformed image are of the same 2Ddimensions. The sparse, frequency domain representation of convolutionalweighting kernel 314 is stored in memory 316. The sparse representationincludes sparse matrices 312 and a dense matrix 318. In some examples, asparse matrix is determined for each layer of kernel 314. That is, foran 11×11×32 3D kernel, there are 32 sparse, 11×11 matrices. Inconvolutional weighting portion 310, the sparse matrices are multipliedby the transformed input image, and dense matrix 318 is multiplied aftersubsequent convolutional layers 320 or fully connected layers 322.

A nonlinear function is applied in portion 324. In some examples, thenonlinear function is a frequency domain function (discussed in moredetail below). Response normalization is performed in portion 326, andspatial pooling is performed in portion 328. Various convolutionallayers can omit response normalization and/or spatial pooling. An output330 is provided to subsequent convolutional layers 320.

An output 332 of subsequent convolutional layers 320 is provided tofully connected layers 322. Fully connected layers 322 output anextracted feature vector 334 (or other arrangement of extractedfeatures) that is provided to one or more classifiers 336, which can be,for example, linear classifiers. Classifiers 336 can determine, forexample, whether input image 302 contains an object of interest. Memory316 can also store sparse, frequency domain representations 338 ofkernels 340 used in subsequent convolutional layers 320. In someexamples kernels 340 and 314 are the same. In other examples, differentkernels are used in different convolutional layers.

Memory 316 can also store sparse representations 342 of fully connectedlayer coefficients 344. In some examples, coefficients 344 are notstored as sparse representations 342. Memory 316 can also storeclassifier parameters 346 that are used by classifier 336 in classifyinginput image 302 based on the extracted features in feature vector 334.

As discussed above, remaining in the frequency domain after multiplyingsparse matrix 312 with the frequency domain representation of the inputimage eliminates the computationally intensive inverse FFT (IFFT). Inthis way, many operations can be performed in the frequency domain, anda single IFFT can be performed after subsubsequent convolutional layers320 and/or after the last fully connected layer of fully connectedlayers 322.

In order to remain in the frequency domain, the nonlinear function infirst convolutional layer 306 (and in subsequent convolutional layers320) is converted to a frequency domain function. A convolutional layercan be viewed as applying a certain nonlinear function g(y) to an inputfunction f(x), so to determine a frequency domain nonlinear function,the Fourier transform F(g(f(x)) with respect to F(f(x)) can bedetermined. As a specific example, consider the ReLU nonlinear function,where g(y)=ReLU(y). ReLU (also written as ReLu) acts to clip data in thetime domain. It creates sharp corners in the signal, so in the frequencydomain this adds higher frequency harmonics to the spectrum.

Mathematically, ReLu(f(x)) can be expressed through f(x) as amultiplication with the sign(f(x)): which is equal to 1 if f(x)>0 and 0otherwise:

ReLu(f(x))=max{f(x),0}=H[f(x)]*f(x)  (3)

where H is the Heaviside function.

Because f(x) has a limited number of samples, ReLu can be expressedthrough a multiplication with a sum of delta functions:

H[f(x)]*f(x)=f(x)*Σ_(i)δ(x−x _(i)),f(x _(i))>0  (4)

where δ is a delta function.

The Fourier transform of a delta function is given by:

F(δ(x−x ₀))(k)=e ^(2πjk x) ⁰   (5)

Using the linearity of FFTs and the convolution theorem, the Fouriertransform of ReLu(f(x)) can be expressed through the Fourier transformof f(x):

F(ReLU(f(x)))(k)=(Σ_(i) e ^(2πjkx) ^(i) )

F(f(x))  (6)

This shows that in the frequency domain, ReLu( ) acts as a convolutionwith the function of known form. However, this function depends on theinput, so positions are found in the x space domain: fx_(i)>0. This canbe accomplished by taking the inverse transforms of the input andsolving the inequality. Thus, once x has been found, the transferfunction of the ReLu is known for this input, and FFTs do not need to becalculated.

This is illustrated by the following example. Assume an input imagehaving red, green, and blue portions, each being multiplied by afrequency domain representation of a convolutional weighting kernel (K₁,K₂, and K₃). Without a frequency domain nonlinear function, after thefrequency domain representation of the image (I) is multiplied by afrequency domain representation of the kernel, the result (F₁, F₂, F₃)is in the frequency domain. An IFFT is then used to transform the resultto the time domain (f₁, f₂, f₃), and the ReLu function is applied togenerate g₁, g₂, and g₃. Using an FFT, frequency domain outputs G₁, G₂,and G₃ are determined. These outputs serve as inputs to the nextconvolutional layer. This is shown below in equation group (7).

$\begin{matrix}\begin{matrix}{I\;} & I & I \\{F_{1} = {I \times K_{1}}} & {F_{2} = {I \times K_{2}}} & {F_{3} = {I \times K_{3}}} \\{f_{1} = {{IFFT}\left( F_{1} \right)}} & {f_{2} = {{IFFT}\left( F_{2} \right)}} & {f_{3} = {{IFFT}\left( F_{3} \right)}} \\{g_{1} = {{ReLu}\left( f_{1} \right)}} & {g_{2} = {{ReLu}\left( f_{2} \right)}} & {g_{3} = {{ReLu}\left( f_{3} \right)}} \\G_{1} & G_{2} & G_{3}\end{matrix} & (7)\end{matrix}$

G₁, G₂, and G₃ are the output of the layer and input to a next layer asshown below in equation group (8).

$\begin{matrix}\begin{matrix}{T_{1} = {G_{1} \times K_{4}}} & {T_{2} = {G_{2} \times K_{5}}} & {T_{3} = {G_{3} \times K_{6}}} \\{k_{1} = {{IFFT}\left( T_{1} \right)}} & {k_{2} = {{IFFT}\left( T_{2} \right)}} & {k_{3} = {{IFFT}\left( T_{3} \right)}} \\{h_{1} = {{ReLu}\left( k_{1} \right)}} & {h_{2} = {{ReLu}\left( k_{2} \right)}} & {h_{3} = {{ReLu}\left( k_{3} \right)}}\end{matrix} & (8)\end{matrix}$

As with the previous layer, the next layer will need frequency domaininputs (H₁, H₂, and H₃) to multiply with the frequency domainrepresentation of the convolutional weighting kernel. In equation group8, K₄, K₅, and K₆ are frequency domain convolutional weighting kernelsand can be the same as or different from K₁, K₂, and K₃. Because of thenature of the ReLu function, discussed above with respect to deltafunctions, equation group (9) can be determined and used instead oftaking the approach in equation group (7) and applying the IFFT andthen, prior to the next stage, the FFT.

$\begin{matrix}\begin{matrix}I & I & I \\{F_{1} = {I \times K_{1}}} & {F_{2} = {I \times K_{2}}} & {F_{3} = {I \times K_{3}}} \\{G_{1} = {\sum{e*\left( {I \times K_{1}} \right)}}} & {G_{2} = {\sum{e*\left( {I \times K_{2}} \right)}}} & {G_{3} = {\sum{e*\left( {I \times K_{3}} \right)}}}\end{matrix} & (9)\end{matrix}$

Thus, we can avoid invoking the IFFT in every stage of the computation.Although the ReLU nonlinear function is used here as an example, theapproach also applies to other nonlinear functions.

FIG. 4 illustrates a hardware-level block diagram of an examplehardware-efficient convolutional neural network system 400. Imagescaptured from a camera 402 are buffered in a video-frame memory 404. Theframes can be processed sequentially in a first-in first-out (FIFO)order. A frame-raster controller 406 reads pixels from the frame that isbeing processed in a raster order. The pixels are sent into an FFTstreamer 408 that locally buffers the pixel and produces the Fouriertransform of the image. FFT streamer 408 processes sets of pixels of asize that depends on the number of points used in the FFT block. Forexample, a 1024 point FFT would require 1024 pixels to be buffered andprocessed. The FFT of the image is streamed one pixel at a time from FFTstreamer 408. The Fourier transformed pixels are processed by the layers410, 412, and 414 of the convolutional neural network system 400. Layers410, 412, and 414 include multiple convolutional layers and can alsoinclude at least one fully connected layer.

In (first) convolutional layer 410, a Hadamard product is determinedbased on the Fourier transform of the image (or the output of theprevious stage) and the sparsely-represented coefficients of the filtermaps (kernel weights) used in that layer that are stored in memory 416.The kernel weights in the respective layers are transformed to theFourier domain and represented using a sparse decomposition that canemploy a linear combination of sparse matrices weighted by a densematrix. The sparse matrices are read out of memory 416 sequentiallyusing an address controller 418.

The Fourier transformed pixel is multiplied at multiplier 420 with thesparse coefficients at the corresponding location. The outputs of themultiplier 420 are accumulated using control clocks 422, shown as Φ₁ andΦ₂. The latter clock depends on the number of sparse matrices. If thesparse representation has k sparse matrices, Φ₂ ticks once after every kticks of Φ₁. The registered summation of the Hadamard products (i.e.,output of Φ₂) is passed on to a nonlinear block 424, which applies thenon-linear transformation in the Fourier domain and produces thetransformed output for layer 410. This process continues for up to Nstages (as represented by layer 414) for a convolutional neural networkof depth N.

The output of the final convolutional layer (e.g., layer 414) multiplieswith collapsed dense matrix coefficients stored in memory 426 atmultiplier 428. The dense matrix used at this point is the collapsedversion (product of) the multiple dense matrices obtained from thecoefficients of the individual convolutional layers. The dense matrixelements are stored in memory 426 and pulled in a non-linear manner byaddress controller 430. The output is again multiplied with thecoefficients of the fully-connected layer(s) at multiplier 432. Thecoefficients of the fully-connected layer(s) are stored in memory 434and are addressed sequentially by address controller 436. Memory 426 andmemory 434 can be part of a same memory. It can be difficult to combinethe multiplications of multipliers 428 and 432, as the multiplication ofmultiplier 428 is a matrix-matrix multiplication and the multiplicationof multiplier 432 is a scalar-vector multiplication. The output ofmultiplier 432 is a vector 438 of extracted features. These areregistered in a local buffer (not shown) and form the feature vectorthat is used by classifier 440.

Although memory 416 is shown as DRAM, memory 426 is shown as SRAM, andmemory 434 is shown as SRAM, various types of memory can be used formemory 416, 426, and 434. Memory types, and the data stored in variousmemory types, are discussed in more detail with respect to FIG. 2. Videoframe memory 404 can be DRAM, SRAM, structured memory such as CAM, orother memory. Clocks 422, multipliers 420, 428, 432, and other hardwareillustrated in FIG. 4 can be part of an application-specific integratedcircuit (ASIC), field programmable gate array (FPGA), or otherprocessing unit.

In some examples, a set of parallel multiply-accumulate (MAC) units ineach convolutional layer can be used to speed up the computation. Also,parallel multiplier units can be used in the fully-connected anddense-matrix multiplication stages. A parallel set of classifiers canalso be used. Such parallelization methods have the potential to speedup the computation even further at the cost of added control complexity.

FIG. 5 illustrates an image recognition method 500. In process block502, an input image is received. A frequency domain representation ofthe input image is generated in process block 504. In process block 506,a plurality of features are extracted in a convolutional neural network.The features are extracted based at least in part on the frequencydomain representation of the input image and a sparse, frequency domainrepresentation of a convolutional weighting kernel. The sparse,frequency domain representation of the convolutional weighting kernelcomprises a dense matrix and one or more sparse matrices. Process block506 can also comprise performing convolutional processing in aconvolutional portion of the convolutional neural network, and, based onan output of the convolutional processing, performing fully connectedprocessing in a fully connected portion of the convolutional neuralnetwork, where the output of the fully connected processing includes theextracted features. Details of feature extraction as performed inprocess block 506 are discussed with respect to FIGS. 1-4. In processblock 508, the input image is classified based on the plurality ofextracted features. Based on the classification, the input image isidentified as containing an object of interest in process block 510.

FIG. 6 illustrates an image recognition method 600. In process block602, an input image is received. In process block 604, a frequencydomain representation of the input image is generated (e.g., by using anFFT). In process block 606, a sparse, frequency domain representation ofa convolutional weighting kernel is determined. The sparse, frequencydomain representation comprises one or more sparse matrices and a densematrix. In a plurality of convolutional layers of a deep convolutionalneural network, in process block 608, the input image is processed basedon the frequency domain representation of the input image, the one ormore sparse matrices, and a frequency domain nonlinear function. In aplurality of fully connected layers of the deep convolutional neuralnetwork, in process block 610, the input image is processed based on anoutput of the plurality of convolutional layers. In process block 612, aplurality of extracted features is determined based on an output of theplurality of fully connected layers. The input image is classified inprocess block 614 based on the extracted features. Based on theclassification, the input image is identified as containing an object ofinterest in process block 616.

FIG. 7 illustrates a method 700 of recognizing images in which prior todetermining the plurality of extracted features, an output of a lastconvolutional layer is multiplied by the dense matrices of the weightingkernels of all of the convolutional layers. In process block 702, aninput image is received. In process block 704, a frequency domainrepresentation of the input image is generated. In process block 706,sparse matrices and a dense matrix are determined that represent aconvolutional weighting kernel. In some examples, a same convolutionalweighting kernel is applied in each convolutional layer. In otherexamples, different convolutional weighting kernels, and thereforedifferent sparse matrices and dense matrix, are used.

In process block 708, processing is performed in a plurality ofconvolutional layers. Processing can be, for example, as described withrespect to FIGS. 1-6. In process block 710, after a last convolutionallayer, the output of the layer is multiplied by the dense matrices ofthe kernels for the convolutional stages (or multiplied by a collapsedversion (product) of the dense matrices). In process block 712,processing is performed in one or more fully connected layers.Coefficients for the fully connected layers can be stored as sparsematrices and a dense matrix, and in process block 714, after a lastfully connected layer, the output of the layer is multiplied by thedense matrices for the fully connected stages (or multiplied by acollapsed version (product) of the dense matrices). Extracted featuresare then output in process block 716, and the input image is classifiedin process block 718 based on the extracted features.

In some examples, additional techniques are used to reduce memory usageand reduce intensity of computation. In some examples, the complexity ofthe Fourier transform is reduced by using a sparse FFT, which subsamplesthe input images to compute the Fourier transform efficiently. Thecomplexity of the sparse FFT algorithm can be reduced to linear to evensub-linear depending on the characteristics of the input image. Thisallows computational energy reductions even in the presence of smallkernel sizes.

In some examples, a convolutional neural network is trained in theFourier domain so that all the kernel weights are obtained in theFourier domain itself. This avoids the need apply the nonlinear functionin the frequency domain.

Example Computing Systems

FIG. 8 depicts a generalized example of a suitable computing system 800in which the described innovations may be implemented. The computingsystem 800 is not intended to suggest any limitation as to scope of useor functionality, as the innovations may be implemented in diversegeneral-purpose or special-purpose computing systems.

With reference to FIG. 8, the computing system 800 includes one or moreprocessing units 810, 815 and memory 820, 825. In FIG. 8, this basicconfiguration 830 is included within a dashed line. The processing units810, 815 execute computer-executable instructions. A processing unit canbe a general-purpose central processing unit (CPU), processor in anapplication-specific integrated circuit (ASIC), or any other type ofprocessor. In a multi-processing system, multiple processing unitsexecute computer-executable instructions to increase processing power.For example, FIG. 8 shows a central processing unit 810 as well as agraphics processing unit or co-processing unit 815. The tangible memory820, 825 may be volatile memory (e.g., registers, cache, RAM),non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or somecombination of the two, accessible by the processing unit(s). The memory820, 825 stores software 880 implementing one or more innovationsdescribed herein, in the form of computer-executable instructionssuitable for execution by the processing unit(s). For example, memory820, 825 can store time-domain-to-frequency-domain converter 110,feature extractor 114, and classifier 118 of FIG. 1 and/ortime-domain-to-frequency-domain converter 206, feature extractor 208,and classifier 210 of FIG. 2.

A computing system may have additional features. For example, thecomputing system 800 includes storage 840, one or more input devices850, one or more output devices 860, and one or more communicationconnections 870. An interconnection mechanism (not shown) such as a bus,controller, or network interconnects the components of the computingsystem 800. Typically, operating system software (not shown) provides anoperating environment for other software executing in the computingsystem 800, and coordinates activities of the components of thecomputing system 800.

The tangible storage 840 may be removable or non-removable, and includesmagnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any othermedium which can be used to store information and which can be accessedwithin the computing system 800. The storage 840 stores instructions forthe software 880 implementing one or more innovations described herein.For example, storage 840 can store time-domain-to-frequency-domainconverter 110, feature extractor 114, and classifier 118 of FIG. 1and/or time-domain-to-frequency-domain converter 206, feature extractor208, and classifier 210 of FIG. 2.

The input device(s) 850 may be a touch input device such as a keyboard,mouse, pen, or trackball, a voice input device, a scanning device, oranother device that provides input to the computing system 800. Forvideo encoding, the input device(s) 850 may be a camera, video card, TVtuner card, or similar device that accepts video input in analog ordigital form, or a CD-ROM or CD-RW that reads video samples into thecomputing system 800. The output device(s) 860 may be a display,printer, speaker, CD-writer, or another device that provides output fromthe computing system 800.

The communication connection(s) 870 enable communication over acommunication medium to another computing entity. The communicationmedium conveys information such as computer-executable instructions,audio or video input or output, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia can use an electrical, optical, RF, or other carrier.

The innovations can be described in the general context ofcomputer-executable instructions, such as those included in programmodules, being executed in a computing system on a target real orvirtual processor. Generally, program modules include routines,programs, libraries, objects, classes, components, data structures, etc.that perform particular tasks or implement particular abstract datatypes. The functionality of the program modules may be combined or splitbetween program modules as desired in various embodiments.Computer-executable instructions for program modules may be executedwithin a local or distributed computing system.

The terms “system” and “device” are used interchangeably herein. Unlessthe context clearly indicates otherwise, neither term implies anylimitation on a type of computing system or computing device. Ingeneral, a computing system or computing device can be local ordistributed, and can include any combination of special-purpose hardwareand/or general-purpose hardware with software implementing thefunctionality described herein.

For the sake of presentation, the detailed description uses terms like“determine” and “use” to describe computer operations in a computingsystem. These terms are high-level abstractions for operations performedby a computer, and should not be confused with acts performed by a humanbeing. The actual computer operations corresponding to these terms varydepending on implementation.

Example Mobile Devices

FIG. 9 is a system diagram depicting an example mobile device 900including a variety of optional hardware and software components, showngenerally at 902. Any components 902 in the mobile device cancommunicate with any other component, although not all connections areshown, for ease of illustration. The mobile device can be any of avariety of computing devices (e.g., cell phone, smartphone, handheldcomputer, Personal Digital Assistant (PDA), etc.) and can allow wirelesstwo-way communications with one or more mobile communications networks904, such as a cellular, satellite, or other network.

The illustrated mobile device 900 can include a controller or processor910 (e.g., signal processor, microprocessor, ASIC, or other control andprocessing logic circuitry) for performing such tasks as signal coding,data processing, input/output processing, power control, and/or otherfunctions. An operating system 912 can control the allocation and usageof the components 902 and support for one or more application programs914. The application programs can include common mobile computingapplications (e.g., email applications, calendars, contact managers, webbrowsers, messaging applications), or any other computing application.The application programs 914 can also include image recognitiontechnology implemented using convolutional neural networks.Functionality 913 for accessing an application store can also be usedfor acquiring and updating application programs 914.

The illustrated mobile device 900 can include memory 920. Memory 920 caninclude non-removable memory 922 and/or removable memory 924. Thenon-removable memory 922 can include RAM, ROM, flash memory, a harddisk, or other well-known memory storage technologies. The removablememory 924 can include flash memory or a Subscriber Identity Module(SIM) card, which is well known in GSM communication systems, or otherwell-known memory storage technologies, such as “smart cards.” Thememory 920 can be used for storing data and/or code for running theoperating system 912 and the applications 914. Example data can includeweb pages, text, images, sound files, video data, or other data sets tobe sent to and/or received from one or more network servers or otherdevices via one or more wired or wireless networks. The memory 920 canbe used to store a subscriber identifier, such as an InternationalMobile Subscriber Identity (IMSI), and an equipment identifier, such asan International Mobile Equipment Identifier (IMEI). Such identifierscan be transmitted to a network server to identify users and equipment.

The mobile device 900 can support one or more input devices 930, such asa touchscreen 932, microphone 934, camera 936, physical keyboard 938and/or trackball 940 and one or more output devices 950, such as aspeaker 952 and a display 954. Other possible output devices (not shown)can include piezoelectric or other haptic output devices. Some devicescan serve more than one input/output function. For example, touchscreen932 and display 954 can be combined in a single input/output device.

The input devices 930 can include a Natural User Interface (NUI). An NUIis any interface technology that enables a user to interact with adevice in a “natural” manner, free from artificial constraints imposedby input devices such as mice, keyboards, remote controls, and the like.Examples of NUI methods include those relying on speech recognition,touch and stylus recognition, gesture recognition both on screen andadjacent to the screen, air gestures, head and eye tracking, voice andspeech, vision, touch, gestures, and machine intelligence. Otherexamples of a NUI include motion gesture detection usingaccelerometers/gyroscopes, facial recognition, 3D displays, head, eye,and gaze tracking, immersive augmented reality and virtual realitysystems, all of which provide a more natural interface, as well astechnologies for sensing brain activity using electric field sensingelectrodes (EEG and related methods). Thus, in one specific example, theoperating system 912 or applications 914 can comprise speech-recognitionsoftware as part of a voice user interface that allows a user to operatethe device 900 via voice commands. Further, the device 900 can compriseinput devices and software that allows for user interaction via a user'sspatial gestures, such as detecting and interpreting gestures to provideinput to a gaming application.

A wireless modem 960 can be coupled to an antenna (not shown) and cansupport two-way communications between the processor 910 and externaldevices, as is well understood in the art. The modem 960 is showngenerically and can include a cellular modem for communicating with themobile communication network 904 and/or other radio-based modems (e.g.,Bluetooth 964 or Wi-Fi 962). The wireless modem 960 is typicallyconfigured for communication with one or more cellular networks, such asa GSM network for data and voice communications within a single cellularnetwork, between cellular networks, or between the mobile device and apublic switched telephone network (PSTN).

The mobile device can further include at least one input/output port980, a power supply 982, a satellite navigation system receiver 984,such as a Global Positioning System (GPS) receiver, an accelerometer986, and/or a physical connector 990, which can be a USB port, IEEE 1394(FireWire) port, and/or RS-232 port. The illustrated components 902 arenot required or all-inclusive, as any components can be deleted andother components can be added.

Example Cloud-Supported Environments

FIG. 10 illustrates a generalized example of a suitable cloud-supportedenvironment 1000 in which described embodiments, techniques, andtechnologies may be implemented. In the example environment 1000,various types of services (e.g., computing services) are provided by acloud 1010. For example, the cloud 1010 can comprise a collection ofcomputing devices, which may be located centrally or distributed, thatprovide cloud-based services to various types of users and devicesconnected via a network such as the Internet. The implementationenvironment 1000 can be used in different ways to accomplish computingtasks. For example, some tasks (e.g., processing user input andpresenting a user interface) can be performed on local computing devices(e.g., connected devices 1030, 1040, 1050) while other tasks (e.g.,storage of data to be used in subsequent processing) can be performed inthe cloud 1010.

In example environment 1000, the cloud 1010 provides services forconnected devices 1030, 1040, 1050 with a variety of screencapabilities. Connected device 1030 represents a device with a computerscreen 1035 (e.g., a mid-size screen). For example, connected device1030 can be a personal computer such as desktop computer, laptop,notebook, netbook, or the like. Connected device 1040 represents adevice with a mobile device screen 1045 (e.g., a small size screen). Forexample, connected device 1040 can be a mobile phone, smart phone,personal digital assistant, tablet computer, and the like. Connecteddevice 1050 represents a device with a large screen 1055. For example,connected device 1050 can be a television screen (e.g., a smarttelevision) or another device connected to a television (e.g., a set-topbox or gaming console) or the like. One or more of the connected devices1030, 1040, 1050 can include touchscreen capabilities. Touchscreens canaccept input in different ways. For example, capacitive touchscreensdetect touch input when an object (e.g., a fingertip or stylus) distortsor interrupts an electrical current running across the surface. Asanother example, touchscreens can use optical sensors to detect touchinput when beams from the optical sensors are interrupted. Physicalcontact with the surface of the screen is not necessary for input to bedetected by some touchscreens. Devices without screen capabilities alsocan be used in example environment 1000. For example, the cloud 1010 canprovide services for one or more computers (e.g., server computers)without displays.

Services can be provided by the cloud 1010 through service providers1020, or through other providers of online services (not depicted). Forexample, cloud services can be customized to the screen size, displaycapability, and/or touchscreen capability of a particular connecteddevice (e.g., connected devices 1030, 1040, 1050).

In example environment 1000, the cloud 1010 provides the technologiesand solutions described herein to the various connected devices 1030,1040, 1050 using, at least in part, the service providers 1020. Forexample, the service providers 1020 can provide a centralized solutionfor various cloud-based services. The service providers 1020 can manageservice subscriptions for users and/or devices (e.g., for the connecteddevices 1030, 1040, 1050 and/or their respective users). The cloud 1010can store images and video frames 1060 used as inputs to imagerecognition systems as described herein and can store dense and sparsematrices 1062.

Example Implementations

Although the operations of some of the disclosed methods are describedin a particular, sequential order for convenient presentation, it shouldbe understood that this manner of description encompasses rearrangement,unless a particular ordering is required by specific language set forthbelow. For example, operations described sequentially may in some casesbe rearranged or performed concurrently. Moreover, for the sake ofsimplicity, the attached figures may not show the various ways in whichthe disclosed methods can be used in conjunction with other methods.

Any of the disclosed methods can be implemented as computer-executableinstructions or a computer program product stored on one or morecomputer-readable storage media and executed on a computing device(e.g., any available computing device, including smart phones or othermobile devices that include computing hardware). Computer-readablestorage media are any available tangible media that can be accessedwithin a computing environment (e.g., one or more optical media discssuch as DVD or CD, volatile memory components (such as DRAM or SRAM), ornonvolatile memory components (such as flash memory or hard drives)). Byway of example and with reference to FIG. 8, computer-readable storagemedia include memory 820 and 825, and storage 840. By way of example andwith reference to FIG. 9, computer-readable storage media include memoryand storage 920, 922, and 924. The term computer-readable storage mediadoes not include signals and carrier waves. In addition, the termcomputer-readable storage media does not include communicationconnections (e.g., 870, 960, 962, and 964).

Any of the computer-executable instructions for implementing thedisclosed techniques as well as any data created and used duringimplementation of the disclosed embodiments can be stored on one or morecomputer-readable storage media. The computer-executable instructionscan be part of, for example, a dedicated software application or asoftware application that is accessed or downloaded via a web browser orother software application (such as a remote computing application).Such software can be executed, for example, on a single local computer(e.g., any suitable commercially available computer) or in a networkenvironment (e.g., via the Internet, a wide-area network, a local-areanetwork, a client-server network (such as a cloud computing network), orother such network) using one or more network computers.

For clarity, only certain selected aspects of the software-basedimplementations are described. Other details that are well known in theart are omitted. For example, it should be understood that the disclosedtechnology is not limited to any specific computer language or program.For instance, the disclosed technology can be implemented by softwarewritten in C++, Java, Pert, JavaScript, Adobe Flash, or any othersuitable programming language. Likewise, the disclosed technology is notlimited to any particular computer or type of hardware. Certain detailsof suitable computers and hardware are well known and need not be setforth in detail in this disclosure.

Furthermore, any of the software-based embodiments (comprising, forexample, computer-executable instructions for causing a computer toperform any of the disclosed methods) can be uploaded, downloaded, orremotely accessed through a suitable communication means. Such suitablecommunication means include, for example, the Internet, the World WideWeb, an intranet, software applications, cable (including fiber opticcable), magnetic communications, electromagnetic communications(including RF, microwave, and infrared communications), electroniccommunications, or other such communication means.

The disclosed methods, apparatus, and systems should not be construed aslimiting in any way. Instead, the present disclosure is directed towardall novel and nonobvious features and aspects of the various disclosedembodiments, alone and in various combinations and sub combinations withone another. The disclosed methods, apparatus, and systems are notlimited to any specific aspect or feature or combination thereof, nor dothe disclosed embodiments require that any one or more specificadvantages be present or problems be solved.

The technologies from any example can be combined with the technologiesdescribed in any one or more of the other examples. In view of the manypossible embodiments to which the principles of the disclosed technologymay be applied, it should be recognized that the illustrated embodimentsare examples of the disclosed technology and should not be taken as alimitation on the scope of the disclosed technology.

We claim:
 1. A convolutional neural network system, comprising: one ormore processors; a memory configured to store a sparse, frequency domainrepresentation of a convolutional weighting kernel; atime-domain-to-frequency-domain converter configured to, by the one ormore processors, generate a frequency domain representation of an inputimage; a feature extractor configured to, by the one or more processors:access the memory, and extract a plurality of features based at least inpart on the sparse, frequency domain representation of the convolutionalweighting kernel and the frequency domain representation of the inputimage; and a classifier configured to, by the one or more processors,determine, based on the plurality of extracted features, whether theinput image contains an object of interest.
 2. The system of claim 1,wherein the feature extractor comprises a plurality of convolutionallayers and a plurality of fully connected layers.
 3. The system of claim2, wherein the memory is a first memory of a first memory type, andfurther comprising a second memory configured to store coefficients forthe plurality of fully connected layers, wherein the second memory is ofa second memory type, and wherein the first memory type has a sloweraccess time or lower energy consumption than an access time or energyconsumption of the second memory type.
 4. The system of claim 3, whereinthe first memory type is DRAM, and wherein the second memory type isSRAM.
 5. The system of claim 3, further comprising a third memoryconfigured to store input image coefficients, wherein the third memoryis of a third memory type and has an access time or energy consumptionbetween the access time or energy consumption of the first memory typeand the access time or energy consumption of the second memory type. 6.The system of claim 2, wherein the sparse, frequency domainrepresentation of the convolutional weighting kernel comprises a densematrix and one or more sparse matrices, and wherein a firstconvolutional layer of the plurality of convolutional layers isconfigured to: multiply the frequency domain representation of the inputimage by the one or more sparse matrices and apply a nonlinear functionto a result of the multiplication.
 7. The system of claim 6, wherein thenonlinear function is a frequency domain function.
 8. The system ofclaim 6, wherein a second convolutional layer of the plurality ofconvolutional layers is configured to: multiply a frequency domainoutput of the first convolutional layer by the one or more sparsematrices and apply a nonlinear function to a result of themultiplication.
 9. The system of claim 2, wherein the sparse, frequencydomain representation of the convolutional weighting kernel comprises adense matrix and one or more sparse matrices, and wherein, and whereinprior to generation, by the feature extractor, of a feature vector ofthe plurality of extracted features, an output of a last convolutionallayer is multiplied by the dense matrix.
 10. The system of claim 1,further comprising a camera configured to capture video, and wherein theinput image is a video frame captured by the camera.
 11. The system ofclaim 10, wherein the system is part of a virtual reality or augmentedreality system.
 12. A method, comprising: receiving an input image;generating a frequency domain representation of the input image; in aconvolutional neural network, extracting a plurality of features basedat least in part on the frequency domain representation of the inputimage and a sparse, frequency domain representation of a convolutionalweighting kernel, wherein the sparse, frequency domain representation ofthe convolutional weighting kernel comprises a dense matrix and one ormore sparse matrices; classifying the input image based on the pluralityof extracted features; and based on the classifying, identifying theinput image as containing an object of interest.
 13. The method of claim12, wherein extracting the plurality of features comprises: performingconvolutional processing in a convolutional portion of the convolutionalneural network; and based on an output of the convolutional processing,performing fully connected processing in a fully connected portion ofthe convolutional neural network, wherein an output of the fullyconnected processing comprises the extracted features.
 14. The method ofclaim 12, wherein the convolutional neural network is a deepconvolutional neural network comprising a plurality of convolutionallayers and at least one fully connected layer.
 15. The method of claim14, wherein extracting the plurality of features comprises, in a firstconvolutional layer of the plurality of convolutional layers,multiplying the frequency domain representation of the input image bythe one or more sparse matrices and applying a nonlinear function to aresult of the multiplying.
 16. The method of claim 14, wherein valuesfor the convolutional weighting kernel are determined through training,wherein the one or more sparse matrices are stored in a first memory ofa first memory type, wherein the dense matrix is stored in a secondmemory of a second memory type, and wherein the first memory type has aslower access time than the second memory type.
 17. The method of claim16, wherein the first memory type has lower energy consumption than thesecond memory type.
 18. One or more computer-readable storage mediastoring computer-executable instructions for recognizing images, therecognizing comprising: receiving an input image; generating a frequencydomain representation of the input image; determining a sparse,frequency domain representation of a convolutional weighting kernel, thesparse, frequency domain representation comprising one or more sparsematrices and a dense matrix; in a plurality of convolutional layers of adeep convolutional neural network, processing the input image based onthe frequency domain representation of the input image, the one or moresparse matrices, and a frequency domain nonlinear function; in aplurality of fully connected layers of the deep convolutional neuralnetwork, processing the input image based on an output of the pluralityof convolutional layers; determining a plurality of extracted featuresbased on an output of the plurality of fully connected layers;classifying the input image based on the plurality of extractedfeatures; and based on the classification, identifying the input imageas containing an object of interest.
 19. The one or morecomputer-readable storage media of claim 17, wherein prior todetermining the plurality of extracted features, an output of a lastconvolutional layer is multiplied by the dense matrix.
 20. The one ormore computer-readable storage media of claim 17, wherein the one ormore sparse matrices are stored in a first memory of a first memorytype, wherein the dense matrix is stored in a second memory of a secondmemory type, and wherein the first memory type has a slower access timethan the second memory type.